Create README.md

README.md

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+ # Arabic syllables recognition with tashkeel.
+This is fine tuned wav2vec2 model to recognize arabic syllables from speech.        
+The model was trained on Modern standard arabic dataset.\
+5-gram language model is available with the model.
+
+To try it out :
+
+```
+!pip install datasets transformers
+!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode
+```
+
+```
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from transformers import Wav2Vec2ProcessorWithLM
+processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
+model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")
+```
+```
+import pandas as pd
+dftest = pd.DataFrame(columns=['audio'])
+import datasets
+from datasets import Dataset
+path ='/content/908-33.wav'
+dftest['audio']=[path]  ## audio path
+dataset = Dataset.from_pandas(dftest)
+```
+```
+import torch
+import torchaudio
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["audio"])
+    print(sampling_rate)
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
+    batch["audio"] = resampler(speech_array).squeeze().numpy()
+    return batch
+```
+```
+import numpy as np
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+test_dataset = dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values).logits
+    print(logits.numpy().shape)
+
+transcription = processor.batch_decode(logits.numpy()).text
+print("Prediction:",transcription[0])
+```
+
+
+
+